Joint robust linear regression and anomaly detection in poisson noise using expectation-propagation

D. Yao, Y. Altmann, S. McLaughlin, M. E. Davies

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In this paper, we propose a new Expectation-Propagation (EP) algorithm to address the problem of joint robust linear regression and sparse anomaly detection from data corrupted by Poisson noise. Adopting an approximate Bayesian approach, an EP method is derived to approximate the posterior distribution of interest. The method accounts not only for additive anomalies, but also for destructive anomalies, i.e., anomalies that can lead to observations with amplitudes lower than the expected signals. Experiments conducted with both synthetic and real data illustrate the potential benefits of the proposed EP method in joint spectral unmixing and anomaly detection in the photon-starved regime of a Lidar system.

Original languageEnglish
Title of host publication2020 28th European Signal Processing Conference (EUSIPCO)
Subtitle of host publicationProceedings
Number of pages5
ISBN (Electronic)9789082797053
Publication statusPublished - 18 Dec 2020
Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
Duration: 24 Aug 202028 Aug 2020

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference28th European Signal Processing Conference, EUSIPCO 2020

Keywords / Materials (for Non-textual outputs)

  • Anomaly detection
  • Approximate Bayesian inference
  • Expectation-Propagation
  • Linear regression
  • Poisson noise


Dive into the research topics of 'Joint robust linear regression and anomaly detection in poisson noise using expectation-propagation'. Together they form a unique fingerprint.

Cite this